QA检索在Langchain中的不同功能

在Langchain中，QA检索（Question Answering Retrieval）的不同功能可以通过以下解决方法来实现。

1. 使用预训练模型：首先，你可以选择使用已经在大规模数据上进行了训练的预训练模型，例如BERT、RoBERTa等。这些模型在语言理解任务上表现出色，并且可以用于QA检索任务。你可以使用Hugging Face的Transformers库来加载和使用这些预训练模型。

from transformers import BertTokenizer, BertForQuestionAnswering

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForQuestionAnswering.from_pretrained('bert-base-uncased')

question = "What is the capital of France?"
context = "The capital of France is Paris."

encoding = tokenizer.encode_plus(question, context, return_tensors='pt')
input_ids = encoding['input_ids']
attention_mask = encoding['attention_mask']

start_scores, end_scores = model(input_ids, attention_mask=attention_mask)

start_index = torch.argmax(start_scores)
end_index = torch.argmax(end_scores)

answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[0][start_index:end_index+1]))
print(answer)  # Output: "Paris"

2. Fine-tuning模型：如果预训练模型的性能不够令人满意，你可以选择对其进行Fine-tuning以适应特定的QA检索任务。你需要为QA检索任务准备自定义的训练数据集，并使用预训练模型作为基础模型进行Fine-tuning。Fine-tuning过程中，你可以使用适当的损失函数（例如交叉熵损失）来训练模型。

from transformers import BertForQuestionAnswering, BertTokenizer, AdamW
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from transformers import squad_convert_examples_to_features
from transformers.data.processors.squad import SquadV2Processor
import torch

# 加载模型和tokenizer
model = BertForQuestionAnswering.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 准备训练数据
processor = SquadV2Processor()
train_examples = processor.get_train_examples('data_dir')  # 从数据集中获取训练样本
train_features, train_dataset = squad_convert_examples_to_features(
    examples=train_examples,
    tokenizer=tokenizer,
    max_seq_length=384,
    doc_stride=128,
    max_query_length=64,
    is_training=True,
    return_dataset='pt'
)
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=8)

# 定义优化器和学习率
optimizer = AdamW(model.parameters(), lr=5e-5)

# Fine-tuning过程
model.train()
model.to(device)
for epoch in range(3):
    for batch in train_dataloader:
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        start_positions = batch['start_positions'].to(device)
        end_positions = batch['end_positions'].to(device)

        outputs = model(input_ids, attention_mask=attention_mask, start_positions=start_positions, end_positions=end_positions)
        loss = outputs.loss
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

# 使用Fine-tuned模型进行QA检索
model.eval()
question = "What is the capital of France?"
context = "The capital of France is Paris."

encoding = tokenizer.encode_plus(question, context, return_tensors='pt')
input_ids = encoding['input_ids'].to(device)
attention_mask = encoding['attention_mask'].to(device)

start_scores, end_scores = model(input_ids, attention_mask=attention_mask)

start_index = torch.argmax(start_scores)
end_index = torch.argmax(end_scores)

answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[0][start_index:end_index+1]))
print(answer)  # Output: "Paris"

这些示例代码展示了如何使用预训练模型和Fine-tuning模型来实现QA检索任务。你可以根据自己的需求和数据集进行相应的调整和修改。